The Role of the Interplay between Stimulus Type and Timing in Explaining BCI-Illiteracy for Visual P300-Based Brain-Computer Interfaces

Abstract

Visual P300-based Brain-Computer Interface (BCI) spellers enable communication or interaction with the environment by flashing elements in a matrix and exploiting consequent changes in end-user's brain activity. Despite research efforts, performance variability and BCI-illiteracy still are critical issues for real world applications. Moreover, there is a quite unaddressed kind of BCI-illiteracy, which becomes apparent when the same end-user operates BCI-spellers intended for different applications: our aim is to understand why some well performers can become BCI-illiterate depending on speller type. We manipulated stimulus type (factor STIM: either characters or icons), color (factor COLOR: white, green) and timing (factor SPEED: fast, slow). Each BCI session consisted of training (without feedback) and performance phase (with feedback), both in copy-spelling. For fast flashing spellers, we observed a performance worsening for white icon-speller. Our findings are consistent with existing results reported on end-users using identical white×fast spellers, indicating independence of worsening trend from users' group. The use of slow stimulation timing shed a new light on the perceptual and cognitive phenomena related to the use of a BCI-speller during both the training and the performance phase. We found a significant STIM main effect for the N1 component on Pz and PO7 during the training phase and on PO8 during the performance phase, whereas in both phases neither the STIM×COLOR interaction nor the COLOR main effect was statistically significant. After collapsing data for factor COLOR, it emerged a statistically significant modulation of N1 amplitude depending to the phase of BCI session: N1 was more negative for icons than for characters both on Pz and PO7 (training), whereas the opposite modulation was observed for PO8 (performance). Results indicate that both feedback and expertise with respect to the stimulus type can modulate the N1 component and that icons require more perceptual analysis. Therefore, fast flashing is likely to be more detrimental for end-users' performance in case of icon-spellers. In conclusion, the interplay between stimulus type and timing seems relevant for a satisfactory and efficient end-user's BCI-experience.

Performance Enhancement of P300 Detection by Multiscale-CNN

“…Furthermore, the subjects were randomly recruited and BCI illiteracy was inevitable. All of these factors compromise the practical implementation of BCI techniques [55]. This requires a robust model with high ITR.…”

Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-being) Applications

“…Third, it is designed for a communication purpose that meets the needs of patients (especially locked-in patients). Because of these advantages, the P300 speller has become a standard BCI application and has been used in investigating various research topics such as performance improvement (e.g., classification accuracy or information transfer rate) (Fazel-Rezai et al, 2012), the lowperformance phenomenon called "BCI-illiteracy" (Carabalona, 2017;Won et al, 2019), calibration-less BCI (Lee et al, 2020), patient study (Guy et al, 2018;Velasco-Álvarez et al, 2019), and UI/UX in BCIs such as stimulation type (Guan et al, 2004), clustering of several characters (Fazel-Rezai and Abhari, 2009), 3D cubes (Qu et al, 2018), and facial based cues (Jin et al, 2012). Indeed, researchers have made great achievements and advancements with the P300 BCI speller.…”

An Open Source-Based BCI Application for Virtual World Tour and Its Usability Evaluation

“…Third, it is designed for a communication purpose that meets the needs of patients (especially locked-in patients). Because of these advantages, the P300 speller has become a standard BCI application and has been used in investigating various research topics such as performance improvement (e.g., classification accuracy or information transfer rate) (Fazel-Rezai et al, 2012 ), the low-performance phenomenon called “BCI-illiteracy” (Carabalona, 2017 ; Won et al, 2019 ), calibration-less BCI (Lee et al, 2020 ), patient study (Guy et al, 2018 ; Velasco-Álvarez et al, 2019 ), and UI/UX in BCIs such as stimulation type (Guan et al, 2004 ), clustering of several characters (Fazel-Rezai and Abhari, 2009 ), 3D cubes (Qu et al, 2018 ), and facial based cues (Jin et al, 2012 ). Indeed, researchers have made great achievements and advancements with the P300 BCI speller.…”